Antibiotic resistance: a physicist’s view

The problem of antibiotic resistance poses challenges across many disciplines. One such challenge is to understand the fundamental science of how antibiotics work, and how resistance to them can emerge. This is an area where physicists can make important contributions. Here, we highlight cases where this is already happening, and suggest directions for further physics involvement in antimicrobial research.Comment: 7 pages, 1 figur

Achievements and new knowledge unraveled by metagenomic approaches

Metagenomics has paved the way for cultivation-independent assessment and exploitation of microbial communities present in complex ecosystems. In recent years, significant progress has been made in this research area. A major breakthrough was the improvement and development of high-throughput next-generation sequencing technologies. The application of these technologies resulted in the generation of large datasets derived from various environments such as soil and ocean water. The analyses of these datasets opened a window into the enormous phylogenetic and metabolic diversity of microbial communities living in a variety of ecosystems. In this way, structure, functions, and interactions of microbial communities were elucidated. Metagenomics has proven to be a powerful tool for the recovery of novel biomolecules. In most cases, functional metagenomics comprising construction and screening of complex metagenomic DNA libraries has been applied to isolate new enzymes and drugs of industrial importance. For this purpose, several novel and improved screening strategies that allow efficient screening of large collections of clones harboring metagenomes have been introduced

Predicting Prokaryotic Ecological Niches Using Genome Sequence Analysis

Automated DNA sequencing technology is so rapid that analysis has become the rate-limiting step. Hundreds of prokaryotic genome sequences are publicly available, with new genomes uploaded at the rate of approximately 20 per month. As a result, this growing body of genome sequences will include microorganisms not previously identified, isolated, or observed. We hypothesize that evolutionary pressure exerted by an ecological niche selects for a similar genetic repertoire in those prokaryotes that occupy the same niche, and that this is due to both vertical and horizontal transmission. To test this, we have developed a novel method to classify prokaryotes, by calculating their Pfam protein domain distributions and clustering them with all other sequenced prokaryotic species. Clusters of organisms are visualized in two dimensions as ‘mountains’ on a topological map. When compared to a phylogenetic map constructed using 16S rRNA, this map more accurately clusters prokaryotes according to functional and environmental attributes. We demonstrate the ability of this map, which we term a “niche map”, to cluster according to ecological niche both quantitatively and qualitatively, and propose that this method be used to associate uncharacterized prokaryotes with their ecological niche as a means of predicting their functional role directly from their genome sequence

Computational Biology Methods and Their Application to the Comparative Genomics of Endocellular Symbiotic Bacteria of Insects

Comparative genomics has become a real tantalizing challenge in the postgenomic era. This fact has been mostly magnified by the plethora of new genomes becoming available in a daily bases. The overwhelming list of new genomes to compare has pushed the field of bioinformatics and computational biology forward toward the design and development of methods capable of identifying patterns in a sea of swamping data noise. Despite many advances made in such endeavor, the ever-lasting annoying exceptions to the general patterns remain to pose difficulties in generalizing methods for comparative genomics. In this review, we discuss the different tools devised to undertake the challenge of comparative genomics and some of the exceptions that compromise the generality of such methods. We focus on endosymbiotic bacteria of insects because of their genomic dynamics peculiarities when compared to free-living organisms

Evolutionarily Conserved Substrate Substructures for Automated Annotation of Enzyme Superfamilies

The evolution of enzymes affects how well a species can adapt to new environmental conditions. During enzyme evolution, certain aspects of molecular function are conserved while other aspects can vary. Aspects of function that are more difficult to change or that need to be reused in multiple contexts are often conserved, while those that vary may indicate functions that are more easily changed or that are no longer required. In analogy to the study of conservation patterns in enzyme sequences and structures, we have examined the patterns of conservation and variation in enzyme function by analyzing graph isomorphisms among enzyme substrates of a large number of enzyme superfamilies. This systematic analysis of substrate substructures establishes the conservation patterns that typify individual superfamilies. Specifically, we determined the chemical substructures that are conserved among all known substrates of a superfamily and the substructures that are reacting in these substrates and then examined the relationship between the two. Across the 42 superfamilies that were analyzed, substantial variation was found in how much of the conserved substructure is reacting, suggesting that superfamilies may not be easily grouped into discrete and separable categories. Instead, our results suggest that many superfamilies may need to be treated individually for analyses of evolution, function prediction, and guiding enzyme engineering strategies. Annotating superfamilies with these conserved and reacting substructure patterns provides information that is orthogonal to information provided by studies of conservation in superfamily sequences and structures, thereby improving the precision with which we can predict the functions of enzymes of unknown function and direct studies in enzyme engineering. Because the method is automated, it is suitable for large-scale characterization and comparison of fundamental functional capabilities of both characterized and uncharacterized enzyme superfamilies

Mainstreams of Horizontal Gene Exchange in Enterobacteria: Consideration of the Outbreak of Enterohemorrhagic E. coli O104:H4 in Germany in 2011

Escherichia coli O104:H4 caused a severe outbreak in Europe in 2011. The strain TY-2482 sequenced from this outbreak allowed the discovery of its closest relatives but failed to resolve ways in which it originated and evolved. On account of the previous statement, may we expect similar upcoming outbreaks to occur recurrently or spontaneously in the future? The inability to answer these questions shows limitations of the current comparative and evolutionary genomics methods.status: publishe

The neuropeptide NMU amplifies ILC2-driven allergic lung inflammation

Type 2 innate lymphoid cells (ILC2s) both contribute to mucosal homeostasis and initiate pathologic inflammation in allergic asthma. However, the signals that direct ILC2s to promote homeostasis versus inflammation are unclear. To identify such molecular cues, we profiled mouse lung-resident ILCs using single-cell RNA sequencing at steady state and after in vivo stimulation with the alarmin cytokines IL-25 and IL-33. ILC2s were transcriptionally heterogeneous after activation, with subpopulations distinguished by expression of proliferative, homeostatic and effector genes. The neuropeptide receptor Nmur1 was preferentially expressed by ILC2s at steady state and after IL-25 stimulation. Neuromedin U (NMU), the ligand of NMUR1, activated ILC2s in vitro, and in vivo co-administration of NMU with IL-25 strongly amplified allergic inflammation. Loss of NMU-NMUR1 signalling reduced ILC2 frequency and effector function, and altered transcriptional programs following allergen challenge in vivo. Thus, NMUR1 signalling promotes inflammatory ILC2 responses, highlighting the importance of neuro-immune crosstalk in allergic inflammation at mucosal surfaces

Multiple Data Analyses and Statistical Approaches for Analyzing Data from Metagenomic Studies and Clinical Trials

Metagenomics, also known as environmental genomics, is the study of the genomic content of a sample of organisms (microbes) obtained from a common habitat. Metagenomics and other “omics” disciplines have captured the attention of researchers for several decades. The effect of microbes in our body is a relevant concern for health studies. There are plenty of studies using metagenomics which examine microorganisms that inhabit niches in the human body, sometimes causing disease, and are often correlated with multiple treatment conditions. No matter from which environment it comes, the analyses are often aimed at determining either the presence or absence of specific species of interest in a given metagenome or comparing the biological diversity and the functional activity of a wider range of microorganisms within their communities. The importance increases for comparison within different environments such as multiple patients with different conditions, multiple drugs, and multiple time points of same treatment or same patient. Thus, no matter how many hypotheses we have, we need a good understanding of genomics, bioinformatics, and statistics to work together to analyze and interpret these datasets in a meaningful way. This chapter provides an overview of different data analyses and statistical approaches (with example scenarios) to analyze metagenomics samples from different medical projects or clinical trials

The great screen anomaly—a new frontier in product discovery through functional metagenomics

Functional metagenomics, the study of the collective genome of a microbial community by expressing it in a foreign host, is an emerging field in biotechnology. Over the past years, the possibility of novel product discovery through metagenomics has developed rapidly. Thus, metagenomics has been heralded as a promising mining strategy of resources for the biotechnological and pharmaceutical industry. However, in spite of innovative work in the field of functional genomics in recent years, yields from function-based metagenomics studies still fall short of producing significant amounts of new products that are valuable for biotechnological processes. Thus, a new set of strategies is required with respect to fostering gene expression in comparison to the traditional work. These new strategies should address a major issue, that is, how to successfully express a set of unknown genes of unknown origin in a foreign host in high throughput. This article is an opinionating review of functional metagenomic screening of natural microbial communities, with a focus on the optimization of new product discovery. It first summarizes current major bottlenecks in functional metagenomics and then provides an overview of the general metagenomic assessment strategies, with a focus on the challenges that are met in the screening for, and selection of, target genes in metagenomic libraries. To identify possible screening limitations, strategies to achieve optimal gene expression are reviewed, examining the molecular events all the way from the transcription level through to the secretion of the target gene product

A statistical toolbox for metagenomics: assessing functional diversity in microbial communities

<p>Abstract</p> <p>Background</p> <p>The 99% of bacteria in the environment that are recalcitrant to culturing have spurred the development of metagenomics, a culture-independent approach to sample and characterize microbial genomes. Massive datasets of metagenomic sequences have been accumulated, but analysis of these sequences has focused primarily on the descriptive comparison of the relative abundance of proteins that belong to specific functional categories. More robust statistical methods are needed to make inferences from metagenomic data. In this study, we developed and applied a suite of tools to describe and compare the richness, membership, and structure of microbial communities using peptide fragment sequences extracted from metagenomic sequence data.</p> <p>Results</p> <p>Application of these tools to acid mine drainage, soil, and whale fall metagenomic sequence collections revealed groups of peptide fragments with a relatively high abundance and no known function. When combined with analysis of 16S rRNA gene fragments from the same communities these tools enabled us to demonstrate that although there was no overlap in the types of 16S rRNA gene sequence observed, there was a core collection of operational protein families that was shared among the three environments.</p> <p>Conclusion</p> <p>The results of comparisons between the three habitats were surprising considering the relatively low overlap of membership and the distinctively different characteristics of the three habitats. These tools will facilitate the use of metagenomics to pursue statistically sound genome-based ecological analyses.</p